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Facilitator Guide — AI-Assisted Development Bootcamp

For: Dev Team Leads facilitating the bootcamp for their teams
Version: 1.1
Last Updated: January 20, 2026


Document Change Log

Version Date Changes
1.1 2026-01-20 Expanded Week 1 with detailed lab-by-lab facilitation instructions
1.0 2026-01-20 Initial comprehensive facilitator guide

About This Guide

You are about to facilitate your team through a 12-week journey into AI-assisted development. You are not expected to be an AI expert. You will learn alongside your team by observing their execution, guiding discussions, and capturing insights.

This guide provides:

  • Pre and post instructions for every activity
  • What to expect as a team lead new to AI
  • Signs of healthy vs concerning adoption patterns
  • Templates to capture team insights
  • Tie-backs and tie-forwards to connect learning

💡 Your Role: Guide the discovery, don't provide the answers. Your value is in facilitation, observation, and coaching—not AI expertise.


Table of Contents

  1. Before You Begin
  2. What to Expect as a Team Lead (New to AI)
  3. Week-by-Week Facilitation
  4. Lab Facilitation Template
  5. Workshop Facilitation Template
  6. Sprint Facilitation
  7. Signs to Watch For
  8. Insight Capture Templates
  9. Common Struggles & Interventions
  10. Your Learning Journey

1. Before You Begin

1.1 Pre-Bootcamp Checklist (1 Week Before)

Task Owner Status
Confirm all team members have GitHub Copilot Enterprise access You
Verify team has completed .REQUIREMENTS.md prerequisites Team
Review .MASTER_BOOTCAMP.md for program overview You
Skim Week 1 content (don't study—you'll learn by observing) You
Schedule recurring team sessions for labs and workshops You
Set up a shared channel for async questions You
Prepare your Insight Capture document (Section 8) You
Read Section 2 of this guide thoroughly You

1.2 Mindset Preparation

Old mindset (drop this):

  • "I need to know all the answers before the team asks"
  • "I should be the AI expert since I'm leading"
  • "If I don't understand something, I've failed"

New mindset (embrace this):

  • "I guide the discovery process, not the content"
  • "I learn by watching my team's experiments"
  • "My observations about team dynamics are invaluable"
  • "Questions I can't answer become team investigations"

2. What to Expect as a Team Lead (New to AI)

2.1 The Emotional Journey

Phase What You'll Feel What's Actually Happening
Week 1 Overwhelmed, imposter syndrome Normal—everyone is learning
Week 2 Cautious optimism Patterns start emerging
Week 3-4 Growing confidence You see the system working
Sprints Pride in team growth Your facilitation made this possible

2.2 Velocity Changes to Expect

Week 1-2:  ████░░░░░░  Slower (learning curve)
Week 3-4:  ██████░░░░  Recovering (habits forming)
Sprint 1:  ████████░░  Accelerating (confidence)
Sprint 4:  ██████████  Full velocity (AI-augmented)

What causes the initial slowdown:

  • Learning to write effective prompts
  • Building trust (and healthy skepticism) in AI output
  • Establishing new review patterns
  • Forming team conventions

Your job: Protect the team from pressure during the slowdown. This investment pays off.

2.3 Quality Patterns Evolution

Stage Behavior Your Response
Over-reliance Team accepts AI output without question Probe: "How did you verify this?"
Rejection Team dismisses AI as useless Explore: "What specifically didn't work?"
Skepticism Team questions everything (healthy!) Encourage: "Good instinct—document what you caught"
Balance Team uses AI as a tool, not oracle Celebrate: "This is the target state"

2.4 Team Dynamics to Anticipate

Persona Behavior How to Help
The Enthusiast Adopts immediately, may over-rely Channel energy into verification practices
The Skeptic Resists, finds flaws Make them the quality champion
The Silent One Doesn't engage visibly Check in privately, they may be processing
The Perfectionist Spends too long on prompts Set timeboxes, "good enough" is fine
The Rusher Uses AI to skip thinking Slow down with "explain your approach"

2.5 Your Own Learning Curve

As you facilitate, you will learn:

  • Which prompts work for your domain
  • Your team's specific strengths and gaps
  • Patterns that emerge across developers
  • What the rubrics really mean in practice

Capture these learnings. They're valuable for your organization's AI journey.


3. Week-by-Week Facilitation


Week 1: AI Enablement Foundations (DETAILED)

🔙 Tie-Back: None (this is the beginning of the AI journey)

🔜 Tie-Forward: "The habits you build this week—plan first, small diffs, verify everything—are the foundation for everything that follows. In Week 4, you'll use these same patterns to generate contracts. In the sprints, these habits become second nature."

Week 1 Objective: Move the team from "AI curiosity" to "safe, consistent team practice"


Day 0: Lead Preparation (60-90 minutes before Week 1)

This is YOUR prep day. Complete before the team starts.

Task Time Details
Create training branch 15 min Isolate Week 1 work from main codebase
Add repo rails 20 min .github/copilot-instructions.md, path instructions, PR template
Verify example folders 10 min Check /working/frontend/example_lab*/ and /working/backend/example_lab*/ exist
Create lab issues 20 min Create GitHub issues for Labs 0-6, assign to team members
Confirm prerequisites 15 min Verify team completed PRE_WEEK_CHECKLIST.md
Prepare Lab 1 tasks 10 min Have 3-5 simple improvement requests ready for Lab 1

Your Mindset Going In:

  • You don't need to know all the answers
  • Your job is to guide the process, not be the AI expert
  • You'll learn the most by watching your team experiment
  • Take notes on what surprises you

Day 1: Kickoff + Safety + Workflow Discipline

Training: 75-90 minutes | Labs: 30-60 minutes


Session 1: Copilot Reality (30 minutes)

Pre-Session Checklist:

  • Screen sharing ready for demo
  • Simple code change prepared for live demo
  • IDE with Copilot enabled and tested

Key Messages to Deliver:

  1. "Copilot drafts; humans approve"
  2. "Plan → Implement → Verify is mandatory"
  3. "We ship small diffs"

Live Demo Script (10 minutes):

1. Open a simple file
2. Open Copilot Chat
3. Use P0 prompt: "Plan only. Do not generate code. 
   Suggest a small improvement to this file."
4. Review the plan WITH the team
5. Ask: "Is this plan complete? What's missing?"
6. Only then implement ONE step
7. Run tests
8. Show the small diff

Discussion Questions:

  • "What did you notice about how I interacted with Copilot?"
  • "Why did I ask for a plan before coding?"
  • "What would have happened if I just said 'make it better'?"

Signs of Understanding:

  • ✅ Team asks clarifying questions
  • ✅ Someone mentions "what if the plan is wrong?"
  • ⚠️ Team seems ready to skip planning
  • ⚠️ "Can't we just let it code?"

Session 2: Safe Prompting (20 minutes)

Pre-Session Checklist:

  • 5 "unsafe prompt" examples ready (see below)
  • Whiteboard/shared doc for rewriting exercise

Unsafe Prompt Examples to Rewrite:

Unsafe Prompt Why It's Unsafe
"Here's our prod database connection string: jdbc:oracle:thin:@prod-db:1521/PRODDB, make it work" Contains production connection details
"The API key is sk-abc123xyz, add it to the config" Exposes real API key
"Use John Smith's SSN 123-45-6789 as test data" Contains PII
"Here's the customer list from production (paste)" Production data exposure
"The admin password is Admin123!, add it to the login" Credentials in prompt

Exercise (10 minutes):

  1. Show each unsafe prompt
  2. Team rewrites to safe version using placeholders
  3. Discuss: "What placeholder would you use?"

Safe Versions:

  • "Use <DATABASE_URL> placeholder for the connection string"
  • "Use <API_KEY> placeholder for the secret"
  • "Use a sample SSN like 000-00-0000 for test data"
  • "Create sample customer data, do not use real data"
  • "Use <ADMIN_PASSWORD> placeholder for credentials"

The Mantra (repeat this often):

"No secrets, no production data. Use placeholders."


Session 3: Proof Culture (20 minutes)

Key Standards to Establish:

Standard What It Means How to Verify
Every PR lists commands run No "I think I ran tests" PR template requires commands
Tests are not optional If it's not tested, it's not done Rubric requires tests
Build passing is minimum bar Red builds block merges CI/CD enforces this

Discussion:

  • "Who has merged code that later broke something?"
  • "How would 'proof culture' have prevented that?"
  • "What's the cost of NOT having tests?"

Introduce the PR Checklist: Show the template at .github/pull_request_template.md and walk through each item.


Lab 0: Setup Verification + First PR

Timebox: 20-30 minutes

Pre-Lab Briefing (5 minutes)

Materials Ready:

What to Say:

"This lab proves your environment works and you can submit a PR. It's intentionally simple—the goal is to establish the workflow, not impress anyone with the code."

🔙 Tie-Back: "This is our starting point—no prior AI skills assumed."

Watch For:

  • Team members who can't get Copilot to respond
  • Anyone skipping the PR template
  • Confusion about working folder structure
During Lab (20-30 minutes)
Time Your Action
0:00 "Start your timer. You have 25 minutes."
0:10 Walk around (or check chat). "Any environment issues?"
0:15 "10 minutes remaining. If stuck, ask a neighbor."
0:20 "5 minutes. Wrap up and open your PR."
0:25 "Time. Let's debrief."

Common Blockers (have solutions ready):

Blocker Solution
Copilot not responding Check license, restart IDE, verify network
Can't find training branch Provide exact git commands
Build fails Pair with someone whose build works
Unsure what tiny change to make Suggest: "Update README with your name"
Post-Lab Debrief (10 minutes)

Discussion Questions:

  1. "How many got a PR open?" (Show of hands—should be everyone)
  2. "What was the hardest part of setup?"
  3. "Did anyone's Copilot suggest something unexpected?"

🔜 Tie-Forward:

"You just proved you can use Copilot and submit a PR. In Lab 1, we'll add discipline: you won't be allowed to code until your plan is approved."

Note for Your Insight Capture:

  • Who finished early? (Potential helpers for struggling team members)
  • Who struggled? (Watch them closely in Lab 1)
  • Any environment issues that need escalation?

Lab 1: Plan Only

Timebox: 20-30 minutes

Pre-Lab Briefing (5 minutes)

Materials Ready:

Improvement Request Examples (assign one per person):

# Task Complexity
1 Add a utility method to format currency Easy
2 Add input validation to an existing method Easy
3 Create a helper for date formatting Easy
4 Add a constant for a magic number in code Easy
5 Improve error message in an existing handler Easy

What to Say:

"The ONLY output of this lab is a plan. You are NOT allowed to write any code until the plan is reviewed and approved. This feels slow, but it's the habit that will save you from bad AI suggestions."

🔙 Tie-Back:

"In Lab 0, you proved your setup works. Now we add discipline—plan before code."

Watch For:

  • Anyone who starts coding immediately (STOP THEM)
  • Plans that don't include file paths
  • Plans missing test steps
During Lab (20-30 minutes)
Time Your Action
0:00 Assign improvement requests. "Start with P0 prompt only."
0:05 Walk around. If anyone is coding: "Show me your approved plan first."
0:10 Review 2-3 plans publicly. "What's missing from this plan?"
0:15 "If your plan is approved, you may implement. Small diff only."
0:25 "Time. Let's debrief."

Plan Quality Checklist (use for reviews):

Element Present?
Files to be changed
Steps (numbered)
Risks identified
Tests to add/run
References repo patterns

Red Flags in Plans:

  • ❌ "Refactor entire module" (too big)
  • ❌ No mention of tests (missing proof)
  • ❌ Invented patterns not in repo (drift risk)
  • ❌ "Step 1: Implement feature" (not a plan)
Post-Lab Debrief (10 minutes)

Discussion Questions:

  1. "Did the plan help you stay focused?"
  2. "What did Copilot miss in the initial plan?"
  3. "How did you refine the plan?"

Share a Good Plan vs Bad Plan: Show the team an example of a good plan (specific, testable, small scope) versus a bad plan (vague, no tests, scope creep).

🔜 Tie-Forward:

"You've learned to get a plan first. Tomorrow in Lab 2, you'll use this skill to scaffold a complete vertical slice—but you'll break it into 2-3 small steps, each planned."


Day 2: Stack Confirmation + Scaffolding Patterns

Training: 45-60 minutes | Labs: 60-90 minutes


Session 1: Prompt Frame (25 minutes)

Pre-Session Checklist:

The Prompt Frame (write on whiteboard):

Every prompt should include:
1. Context (paths, domain)
2. Goal (what you want)
3. Constraints (rules, limits)
4. Acceptance criteria (how you'll know it's done)
5. Verification (how to test)
6. Output location (where it goes)

Exercise: Bad Prompt → Good Prompt

Bad Prompt:

"Add an orders endpoint"

Good Prompt:

"Context: Spring Boot service in src/main/java/com/example/orders Goal: Add GET /api/orders endpoint returning list of orders Constraints: Follow existing controller patterns, use OrderService AC: Returns JSON array of orders, handles empty list, returns 500 on error Verification: Add unit test, run ./mvnw test Output: OrderController.java in controllers package"

Discussion: "Why is the second prompt better? What would Copilot do with the first one?"


Session 2: Team Patterns (20 minutes)

Pre-Session Checklist:

Patterns to Establish:

Area Pattern Why It Matters
FE Routes src/routes/ folder Copilot needs consistent paths
FE State loader/action pattern Avoid mixing data fetching patterns
FE Errors Problem Details format Consistent error handling
BE Layers controller → service → repository Separation of concerns
BE Errors Problem Details (RFC 7807) Industry standard
Domain Order Management entities Shared language

Team Exercise (10 minutes): Using STACK_CONFIRMATION_TEMPLATE.md, have team answer:

  • Java version? Spring Boot version?
  • Node version? Package manager?
  • Test frameworks (FE and BE)?
  • Oracle strategy for local tests?

Lab 2: Vertical Slice Scaffold

Timebox: 60-90 minutes

Pre-Lab Briefing (10 minutes)

Materials Ready:

What to Say:

"This is the first 'real' lab. You'll build a working endpoint with tests. BUT—you must break it into 2-3 steps and get a plan for each step. No big bang commits."

🔙 Tie-Back:

"You learned to plan in Lab 1. Now you'll apply that skill repeatedly—plan step 1, implement step 1, verify. Then plan step 2."

Deliverables Reminder:

Backend Frontend
GET /api/ping → { "status": "ok" } Route /ping with loading/success/error
Global exception handler (Problem Details) Consumes /api/ping
One unit test One component test
(Stretch) GET /api/orders (Stretch) /orders route

Watch For:

  • Big bang commits (all code at once)
  • Skipping the plan step
  • No tests included
  • Hardcoded values instead of patterns
During Lab (60-90 minutes)
Time Your Action
0:00 "Break this into steps. Plan step 1 before coding."
0:15 Check progress. "Show me your step 1 plan."
0:30 "Who has step 1 done and tested? Share what worked."
0:45 "If you're on step 2, keep going. If stuck, pair up."
0:60 "15 minutes for cleanup and PR."
0:75 "Time. Open your PR now."

Intervention Triggers:

Symptom Intervention
30+ minutes with no commit "Show me what you have. Let's break it smaller."
Tests not written yet "Before your next commit, write one test."
Copied large code block "Walk me through what each line does."
Not using Problem Details "Check the example_lab2 folder for the pattern."
Post-Lab Debrief (15 minutes)

Discussion Questions:

  1. "How did breaking into steps help?"
  2. "What did Copilot scaffold well?"
  3. "What did you have to fix?"
  4. "Show us your 'Copilot vs human' notes."

Share One Good Example: Pick a team member who followed the process well. Have them walk through their commits.

🔜 Tie-Forward:

"You can now scaffold code. Tomorrow, we flip it—you'll write tests FIRST, then make them pass. This is where quality really gets baked in."


Day 3: Tests-First Development

Training: 30 minutes | Labs: 60-120 minutes


Session: Tests from Acceptance Criteria (30 minutes)

Pre-Session Checklist:

Key Message:

"Copilot writes decent tests ONLY if you give it crisp acceptance criteria. Vague criteria = useless tests."

Acceptance Criteria Format:

Given [context]
When [action]
Then [expected result]

Example Transformation:

Vague Requirement:

"Orders should work properly"

Crisp Acceptance Criteria:

  • Given an order ID exists, when I call GET /api/orders/{id}, then return the order with customer name and item list
  • Given an order ID does not exist, when I call GET /api/orders/{id}, then return 404 with Problem Details
  • Given an order with items, when I retrieve it, then the total equals sum of item prices

Exercise: Write AC for "Order Status Update" Team practice: Convert "users should be able to update order status" into 3-5 acceptance criteria.


Lab 3: Tests First

Timebox: 60-90 minutes

Pre-Lab Briefing (10 minutes)

Materials Ready:

  • LAB_3_TESTS_FIRST.md visible
  • 3-6 acceptance criteria bullets (provide these OR use samples from lab)
  • Example folder /working/backend/example_lab3/ ready

Acceptance Criteria to Provide (if team lead doesn't have custom ones):

  1. Given an order ID, return the order with customer name and item list
  2. Return 404 with Problem Details if order not found
  3. Order total should be calculated from item prices
  4. Orders can be filtered by status (pending, completed, cancelled)

What to Say:

"You will write tests FIRST. The tests will fail. Then you write the minimum code to make them pass. This is TDD with AI assistance—Copilot helps you write tests, but YOU verify they're correct."

🔙 Tie-Back:

"In Lab 2, you scaffolded code and added tests after. This time, tests come FIRST. The plan-first discipline applies here too—plan your tests before writing them."

Critical Rule:

"If a test passes before you write any implementation, something is wrong. Either the test is bad, or there's leftover code."

During Lab (60-90 minutes)
Time Your Action
0:00 Distribute acceptance criteria. "Use P2 prompt for tests."
0:15 "Who has tests written? Do they fail? (They should!)"
0:30 "Now implement just enough to make ONE test pass."
0:45 "Check: Are you writing more code than needed?"
0:60 "Final push. All tests should pass now."
0:75 "PR time. Include test count and what Copilot got wrong."

Common Test Problems:

Problem What to Say
Tests pass before implementation "That test doesn't actually test anything. What's the assertion?"
Too many tests at once "Just write tests for ONE acceptance criterion first."
Tests don't match AC "Read the AC again. Does your test verify exactly that?"
Copilot generates implementation "Delete that. We're in tests-only mode right now."
Post-Lab Debrief (15 minutes)

Discussion Questions:

  1. "Did writing tests first change how you thought about the code?"
  2. "What did Copilot get wrong in the tests?"
  3. "How much time fixing tests vs writing implementation?"

Key Insight to Reinforce:

"The time you spend writing good tests now saves 10x debugging time later. Tests are not overhead—they're proof."

🔜 Tie-Forward:

"You now have code WITH tests. Tomorrow, we refactor that code—but safely, with guardrails. The tests you wrote today will catch any regression."


Day 4: OpenShift Readiness + Guarded Refactor

Training: 20 minutes | Labs: 90-120 minutes


Session: Guarded Refactoring (20 minutes)

Key Concepts:

Concept Meaning
No behavior change Same inputs → same outputs
Scope control Only touch specified files
Tests verify All existing tests must pass
Small diffs One refactor at a time

What's Allowed vs Not Allowed:

✅ Allowed ❌ Not Allowed
Rename variables Change API signatures
Extract methods Add new features
Remove duplication Modify return types
Improve error messages Change business logic
Add constants Touch unrelated files

Warning:

"The most dangerous phrase in refactoring is 'while I'm in here...' If you see something else to fix, note it and create a separate PR."


Lab 4: Guarded Refactor

Timebox: 45-60 minutes

Pre-Lab Briefing (5 minutes)

Materials Ready:

Refactor Target Examples:

  • Magic numbers → constants
  • Duplicated code → extracted method
  • Long method → split into smaller methods
  • Poor variable names → meaningful names

What to Say:

"Pick ONE small thing to refactor. Get a plan with RISKS identified. If Copilot suggests anything outside your target files, reject it."

🔙 Tie-Back:

"The tests you wrote yesterday are your safety net. If you break something, the tests will catch it."

During Lab (45-60 minutes)
Time Your Action
0:00 Each person identifies their refactor target.
0:10 Review 2-3 refactor plans. "What are the risks?"
0:25 "Run tests. They should all pass. If not, you changed behavior."
0:40 "PR time. Show the before/after diff."

Scope Creep Check: At 0:25, ask: "Did you touch any file that wasn't in your original plan?" If yes, discuss whether that was appropriate.

Post-Lab Debrief (10 minutes)

🔜 Tie-Forward:

"You just proved you can change code safely. In Lab 5, we prepare for production—health probes, config, deployment manifests."


Lab 5: OpenShift Readiness

Timebox: 45-60 minutes

Pre-Lab Briefing (5 minutes)

Materials Ready:

  • LAB_5_OPENSHIFT_READINESS.md visible
  • Example K8s manifests or Helm charts from your org
  • Example folder /working/backend/example_lab5/ ready

What to Say:

"This lab makes your service deployable. Health probes, externalized config, deployment manifests. This is what separates 'it works on my machine' from 'it runs in production.'"

🔙 Tie-Back:

"You've built the feature (Labs 2-3) and refactored it safely (Lab 4). Now we package it for deployment."

Deliverables:

Backend Frontend
Spring Actuator health endpoints Environment-based API URL
Liveness/readiness probes Production build verification
External config (env vars) Static file serving config
K8s manifests (deployment, service, configmap) (Optional) nginx config
During Lab (45-60 minutes)
Time Your Action
0:00 "Start with health endpoints. Verify with curl."
0:20 "Now externalize config. No hardcoded values."
0:35 "Create manifests. Validate with kubectl --dry-run."
0:50 "PR time. Include deployment instructions in README."
Post-Lab Debrief (10 minutes)

🔜 Tie-Forward:

"Tomorrow is the capstone—you'll build a complete feature end-to-end using everything you've learned this week."


Day 5: Capstone + Standardization

Labs: 60-90 minutes | Retrospective: 30 minutes


Lab 6: Capstone Mini-Feature

Timebox: 60-90 minutes

Pre-Lab Briefing (10 minutes)

Materials Ready:

Feature Options:

Feature BE FE
Create Order POST /api/orders with validation Form to submit order
Order Details GET /api/orders/:id with calculated total Detail page
Order Status Update PATCH /api/orders/:id/status Status dropdown
Order List Filter GET /api/orders?status=pending Filtered list view

What to Say:

"This is the culmination of Week 1. You'll deliver a complete feature—backend endpoint with real logic, frontend that consumes it, tests on both sides. Use EVERYTHING you've learned: plan first, small diffs, tests, proof."

🔙 Tie-Back:

"Lab 0 proved your setup. Lab 1 taught planning. Lab 2 scaffolding. Lab 3 tests-first. Lab 4 safe refactoring. Lab 5 deployment readiness. Now you combine ALL of it."

Pair Programming Option: If team wants to pair:

  • Driver (types, prompts Copilot)
  • Navigator (reviews, thinks ahead)
  • Switch every 20-25 minutes
  • Both names in README
During Lab (60-90 minutes)
Time Your Action
0:00 Teams/individuals choose feature. "Plan first!"
0:15 Check plans. "Is it scoped small enough?"
0:30 "BE folks: endpoint working? FE folks: route rendering?"
0:45 "Integration point. Can FE call BE?"
0:60 "Tests on both sides. Run full suite."
0:75 "PR with reflection questions answered."

Integration Checkpoint (0:45): This is the moment where FE and BE connect. Watch for:

  • CORS issues
  • Contract mismatches
  • Different assumptions about data format

If pairs are stuck on integration, help them debug together—this is a valuable teaching moment.

Post-Lab Debrief (15 minutes)

Reflection Questions (require answers in README):

Effectiveness:

  1. What did Copilot do well?
  2. What did you have to fix or reject?
  3. What would you prompt differently next time?

Process: 4. Did plan-first help with the larger feature? 5. How did tests-first contribute to quality? 6. What repo pattern should we add?

Quantitative:

  • Lines generated by Copilot: ___
  • Lines written/fixed by human: ___
  • Suggestions rejected: ___
  • Time prompting vs coding: ___

🔜 Tie-Forward:

"In Week 2, you'll learn to feed Copilot even better context—constraints and requirements. The habits you built this week will accelerate everything that follows."


Week 1 Standardization Retrospective (30 minutes)

Agenda:

Time Activity
0:00 Celebrate completions! Who finished all 7 labs?
0:05 Collect best prompts from the week (add to team library)
0:15 Discuss: What patterns should we add to repo instructions?
0:25 Discuss: What failure modes did we observe? How to prevent?
0:30 Close: Preview Week 2

Prompt Collection Template:

## Prompt: [Name]
**When to use:** [Situation]
**Template:**
[The actual prompt]
**Why it works:** [Explanation]
**Submitted by:** [Name]

Week 1 Gate Criteria

Before moving to Week 2, each team member must have:

  • Lab 0: Setup verified, first PR submitted
  • Lab 1: Plan-first discipline demonstrated
  • Lab 2: Vertical slice scaffolded with tests
  • Lab 3: Tests-first workflow completed
  • Lab 4: Guarded refactor completed
  • Lab 5: OpenShift readiness verified
  • Lab 6: Capstone feature delivered end-to-end
  • All PRs in /working/ folders with proper structure
  • All PRs include verification commands
  • Lab 6 reflection completed

If Someone Doesn't Pass:

  • Identify specific gaps
  • Provide targeted support
  • Allow one more attempt on failed labs
  • Do not move forward until gate is passed

Week 1 Facilitator Checklist

End of each day, check:

Day Checklist
Day 1 ☐ All environments working ☐ Everyone opened first PR ☐ Plan-first understood
Day 2 ☐ Stack confirmed ☐ Vertical slices submitted ☐ Patterns established
Day 3 ☐ Tests-first discipline shown ☐ Everyone wrote tests before code
Day 4 ☐ Safe refactoring demonstrated ☐ Deployment readiness verified
Day 5 ☐ Capstones delivered ☐ Retrospective completed ☐ Prompts collected

End of week, complete:

  • Week 1 Insight Capture (Section 8)
  • Note team members who struggled and plan support
  • Identify patterns to reinforce in Week 2
  • Update team prompt library with discoveries
  • Celebrate the team's progress!

Week 2: Constraints & Requirements

🔙 Tie-Back: "Last week you learned to get plans from AI before coding. This week, you'll learn to feed AI better context—constraints and requirements—so those plans are more accurate."

🔜 Tie-Forward: "The constraints you extract this week become inputs to the architecture decisions in Week 3."

Pre-Week Setup

  • Review WEEK_2_PROGRAM.md
  • Prepare stakeholder scenarios for constraint extraction
  • Have sample user journeys ready if team gets stuck

Key Facilitation Moments

Lab Watch For Intervention If Needed
Lab 2-0: Constraint Extraction Over-constraining or under-constraining "What would happen if we removed this constraint?"
Lab 2-1: Journey Mapping Too technical, not user-focused "Tell me this from the user's perspective"
Lab 2-2: AC Writing Vague criteria "How would you test this acceptance criterion?"
Lab 2-3: Constraint Review Rubber-stamping "Play devil's advocate on this constraint"

Post-Week Reflection

  • Complete Week 2 Insight Capture
  • How well did team translate business needs to technical constraints?
  • Who emerged as strong at requirements elicitation?

Week 3: Architecture with AI

🔙 Tie-Back: "You've extracted constraints and requirements. Now you'll use AI to help make architecture decisions that honor those constraints."

🔜 Tie-Forward: "The ADRs you create this week are referenced in Week 4 contracts and throughout the sprints."

Pre-Week Setup

  • Review WEEK_3_PROGRAM.md
  • Prepare architecture decision scenarios
  • Have ADR template ready

Key Facilitation Moments

Lab Watch For Intervention If Needed
Lab 3-0: ADR Drafting AI generating options without team evaluation "Which option would you choose and why?"
Lab 3-1: Constitution Section Copying AI output verbatim "Does this reflect your team's actual values?"
Lab 3-2: System Spec Persona Generic descriptions "What makes YOUR system unique?"
Lab 3-3: ADR Review Not challenging decisions "What's the strongest argument against this?"

Post-Week Reflection

  • Complete Week 3 Insight Capture
  • How well does team justify decisions vs. accept AI suggestions?
  • Are ADRs actually useful or just checkbox exercises?

Week 4: Contracts & Integration

🔙 Tie-Back: "You have constraints (Week 2) and architecture decisions (Week 3). Now you'll define the contracts that make FE/BE integration work."

🔜 Tie-Forward: "These contracts are the foundation for Sprint 1. Every feature implementation will reference them."

Pre-Week Setup

  • Review WEEK_4_PROGRAM.md
  • Prepare domain glossary inputs
  • Have OpenAPI examples ready for reference

Key Facilitation Moments

Lab Watch For Intervention If Needed
Lab 4-0: OpenAPI Endpoint Inconsistent naming "Does this match our domain glossary?"
Lab 4-1: Glossary Term Ambiguous definitions "Give me an example of this term in use"
Lab 4-2: MSW Handler Hardcoded happy paths only "What about error cases?"
Lab 4-3: Contract Validation Missing error responses "What happens when things go wrong?"

Contract Lock Ceremony

  • Schedule 2-hour session for end of week
  • All team members review final contracts
  • Document any "we'll figure it out later" items
  • Celebrate the milestone!

Post-Week Reflection

  • Complete Week 4 Insight Capture
  • Is contract quality good enough for sprints?
  • What misunderstandings surfaced during contract review?

Sprints 1-4: Execution

🔙 Tie-Back: "For four weeks, you've been building skills and artifacts. Now you apply everything to build real features."

🔜 Tie-Forward: (Sprint-specific, see below)

Sprint Facilitation Pattern

Before Each Sprint:

  • Review sprint packet in docs/content/sprint-XX/
  • Understand the feature scope and NFRs
  • Prepare for standup facilitation

During Sprint:

  • Daily standups (15 min max)
  • Monitor for AI over-reliance or rejection
  • Enforce PR review standards
  • Track progress toward gate criteria

End of Sprint:

  • Demo facilitation
  • Retrospective (use template in Section 8)
  • Gate assessment
  • Insight capture

Sprint-Specific Focus

Sprint Theme Key Watch Points
Sprint 1 Core Features First real integration—expect friction
Sprint 2 Feature Expansion Performance NFRs introduced
Sprint 3 Security Hardening OWASP compliance, may slow team down
Sprint 4 Production Readiness Documentation, polish, deployment

4. Lab Facilitation Template

Use this template for every lab:

Pre-Lab (5-10 minutes before)

## Lab X Pre-Brief

**Lab:** [Name]
**Timebox:** [XX minutes]
**Prompt(s) used:** [P0, P1, etc.]

### 🔙 Tie-Back
"In [previous activity], you learned [skill]. Today we build on that by..."

### Today's Goal
[One sentence: what must be true when done]

### Watch For
- [ ] [Specific behavior to observe]
- [ ] [Common mistake to catch]

### Materials Ready
- [ ] [Reference doc]
- [ ] [Example if stuck]

During Lab

Time Action
Start Brief the team, set timer
25% Quick pulse check: "Any blockers?"
50% Observe silently, take notes
75% "10 minutes remaining"
End Gather for debrief

Post-Lab Debrief (10-15 minutes)

## Lab X Debrief

### 🔜 Tie-Forward
"What you just practiced will be critical in [future activity] because..."

### Discussion Questions
1. "What did Copilot get right? What did it miss?"
2. "How did you verify the AI output?"
3. "What would you do differently next time?"

### Observations (for your notes)
- Who struggled:
- Who excelled:
- Patterns noticed:
- Follow-up needed:

5. Workshop Facilitation Template

Pre-Workshop (Day before)

## Workshop Pre-Brief

**Workshop:** [Name]
**Duration:** [XX minutes]
**Materials:** [List]

### Preparation
- [ ] Room/virtual space booked
- [ ] Materials distributed
- [ ] Breakout groups assigned (if applicable)
- [ ] Timer ready

### 🔙 Tie-Back
"This workshop connects to [previous content] by..."

### Learning Objectives
1. [Objective 1]
2. [Objective 2]
3. [Objective 3]

Workshop Agenda Template

Time Activity Facilitator Notes
0:00 Opening + Tie-Back Set context, energy
0:10 Concept Introduction Keep brief, they learn by doing
0:20 Hands-On Activity Observe, don't help too quickly
0:45 Share-Out Each group presents
0:55 Synthesis + Tie-Forward Connect to next steps
1:00 Close Capture remaining questions

Post-Workshop

## Workshop Debrief (Your Notes)

### What Worked
-

### What to Adjust Next Time
-

### Team Insights Captured
-

### Follow-Up Items
- [ ]

6. Sprint Facilitation

Daily Standup (15 minutes max)

Format:

  1. Yesterday: What did you complete?
  2. Today: What will you work on?
  3. Blockers: What's in your way?
  4. AI Insight (optional): "One thing AI helped with or struggled with"

Your Role:

  • Keep it moving (2 min per person max)
  • Note blockers for follow-up
  • Don't solve problems in standup—schedule follow-ups

Sprint Demo Facilitation

Before Demo:

  • Confirm working environment
  • Prepare demo script with team
  • Invite stakeholders

During Demo:

  • Let developers present their own work
  • Capture stakeholder feedback
  • Note questions for backlog

After Demo:

  • Thank the team publicly
  • Document feedback
  • Feed into retrospective

Sprint Retrospective Template

Duration: 60 minutes

Agenda:

Time Activity
0:00 Set the stage: "What happens here stays here"
0:05 What went well? (sticky notes, 5 min silent)
0:10 Group and discuss (10 min)
0:20 What didn't go well? (sticky notes, 5 min silent)
0:25 Group and discuss (10 min)
0:35 AI-Specific Questions (see below)
0:45 Action items (what will we change?)
0:55 Appreciations
1:00 Close

AI-Specific Retro Questions:

  1. "Where did AI save us the most time?"
  2. "Where did AI lead us astray?"
  3. "What prompts should we add to our team's library?"
  4. "Are we verifying AI output enough? Too much?"
  5. "How has our trust in AI changed this sprint?"

7. Signs to Watch For

Healthy Patterns ✅

Pattern What It Looks Like
Questioning AI output "That looks right, but let me verify..."
Iterative prompting "Let me refine this prompt to get better results"
Test-driven verification Writing tests to validate AI-generated code
Balanced contribution AI helps, human decides
Discussing limitations "AI doesn't understand our specific context here"
Knowledge sharing "I found a prompt that works great for X"
Plan-first discipline Always asking for a plan before implementation

Concerning Patterns ⚠️

Pattern What It Looks Like Intervention
Blind acceptance Copy-paste without reading "Walk me through what this code does"
One-and-done prompting Never refines prompts "What if you gave it more context?"
No verification Merging AI code without tests "How do we know this works?"
AI as oracle "Copilot said so" as justification "Copilot doesn't know our business rules"
Complete rejection "AI is useless, I'll do it myself" "Show me what you tried—let's debug the prompt"
Shortcut seeking Using AI to skip thinking "Explain your approach before asking AI"
Isolation Not sharing what works "Let's share prompts that worked this week"

When to Escalate

Escalate to your manager or program lead if:

  • Team consistently fails gates despite coaching
  • Safety rules are being violated (secrets in prompts)
  • Individual is actively sabotaging adoption
  • Technical blockers you can't resolve

8. Insight Capture Templates

Weekly Insight Capture

Complete this every Friday:

# Week [X] Insight Capture

**Date:** [YYYY-MM-DD]
**Facilitator:** [Your Name]

## Team Pulse (1-5 scale)
- Confidence with AI: [ ]
- Code quality: [ ]
- Velocity: [ ]
- Collaboration: [ ]

## Observations

### What Surprised Me
-

### Strongest Team Member This Week
- Who:
- Why:

### Struggling Team Member This Week
- Who:
- Why:
- My plan to help:

### Prompts That Worked Well
1.
2.

### Prompts That Failed
1.
2.

### Questions I Couldn't Answer
1.
2.

### Patterns I'm Noticing
-

## Action Items for Next Week
- [ ]
- [ ]

Sprint Insight Capture

Complete this at the end of each sprint:

# Sprint [X] Insight Capture

**Date:** [YYYY-MM-DD]
**Facilitator:** [Your Name]

## Delivery
- Stories completed: [ ] / [ ]
- Gate passed: [Yes/No]
- Quality concerns:

## AI Adoption Maturity

### Current State (check one)
- [ ] Over-reliance (accepting too much)
- [ ] Rejection (using too little)
- [ ] Healthy skepticism (questioning appropriately)
- [ ] Balanced augmentation (target state)

### Evidence for Assessment
-

## Team Dynamics

### Collaboration Quality
-

### Who Emerged as Leaders
-

### Who Needs More Support
-

## Recommendations

### For This Team
1.
2.

### For the Program (share with other facilitators)
1.
2.

### For My Own Development
1.
2.

End-of-Program Summary

Complete after Sprint 4:

# Program Summary — [Team Name]

**Facilitator:** [Your Name]
**Date:** [YYYY-MM-DD]

## Overall Journey

### Starting Point (Week 1)
-

### Ending Point (Sprint 4)
-

### Biggest Transformation
-

## Team Assessment

| Member | Starting Skill | Ending Skill | Growth |
|--------|---------------|--------------|--------|
| | | | |

## What Worked Best
1.
2.
3.

## What We'd Do Differently
1.
2.
3.

## Recommendations for Future Cohorts
1.
2.
3.

## Artifacts Worth Sharing
- Prompts:
- Patterns:
- Anti-patterns:

9. Common Struggles & Interventions

Technical Struggles

Struggle Signs Intervention
Copilot not responding Blank suggestions, errors Check license, restart IDE, verify network
Poor suggestions Irrelevant or wrong code Review prompt quality, add context
Can't run tests Build failures Pair with team member, check environment
Merge conflicts Git errors Small commits, frequent pulls

Skill Struggles

Struggle Signs Intervention
Vague prompts "Make it work" style requests Teach specificity: context, goal, constraints
No verification Code goes straight to PR Require test evidence in PRs
Skipping plans Jump to implementation Enforce P0 (plan only) strictly
Scope creep "While I'm here..." changes Redirect: "That's a separate PR"

Mindset Struggles

Struggle Signs Intervention
Fear of AI Avoidance, excuses Private 1:1, explore concerns, small wins
Over-confidence "AI does it all" Introduce failure case, discuss limits
Perfectionism Endless prompt refinement Set timeboxes, "good enough" threshold
Imposter syndrome "Everyone gets it but me" Normalize the learning curve, share your own struggles

Team Struggles

Struggle Signs Intervention
Uneven adoption Some all-in, some resisting Pair enthusiasts with skeptics
Siloed learning No knowledge sharing Weekly "prompt wins" share-out
Blame AI "It's Copilot's fault" Refocus on verification responsibility
Skip collaboration Everyone works alone Introduce pair programming sessions

10. Your Learning Journey

What You'll Learn by Facilitating

By the end of this bootcamp, you will have learned:

  1. How AI changes team dynamics — What to expect, how to coach
  2. Effective prompting patterns — By observing what works for your team
  3. Quality verification practices — How to ensure AI output is safe
  4. Facilitation skills — Running labs, workshops, retrospectives
  5. Your team's strengths and gaps — Invaluable for ongoing coaching

Reflection Prompts for Yourself

Weekly, ask yourself:

  • "What surprised me about how the team used AI this week?"
  • "Where did I want to jump in and 'fix' things? Did I resist?"
  • "What did I learn by observing rather than doing?"
  • "How has my own view of AI-assisted development changed?"

Connecting with Other Facilitators

If multiple teams are going through the bootcamp:

  • Schedule weekly facilitator syncs
  • Share prompts that worked
  • Discuss common struggles
  • Build a facilitator community of practice

After the Bootcamp

Your role doesn't end at Sprint 4:

  • Continue weekly check-ins on AI adoption
  • Maintain the prompt library
  • Onboard new team members to AI practices
  • Share learnings with other teams

Quick Reference Card

The 3 Non-Negotiables (Enforce Always)

  1. Plan first — No code until plan approved (P0)
  2. Small diffs — Keep changes reviewable
  3. Proof — Tests must pass, verification required

Key Prompts to Know

Prompt When Purpose
P0 Every task Plan only, wait for approval
P2 Lab 3+ Tests from acceptance criteria
P4 Lab 4+ Guarded refactor with risks
P20 Gates Gate readiness assessment

Gate Criteria Summary

  • Week Gates: Skills demonstrated, rubric passed
  • Sprint Gates: Features complete, NFRs met, quality verified

Emergency Contacts

  • Technical issues: [Your IT/DevOps contact]
  • Program questions: [Program lead]
  • HR/People issues: [HR contact]

Appendix: Resources

Resource Location
Master Index .MASTER_BOOTCAMP.md
Quick Start .START_HERE.md
Requirements .REQUIREMENTS.md
Domain Context docs/shared/reference-materials/DOMAIN_CONTEXT.md
Prompt Pack docs/shared/reference-materials/PROMPT_PACK_V1.md
Definition of Done docs/shared/reference-materials/DEFINITION_OF_DONE.md
Week 1 Program docs/content/week-01/WEEK_1_PROGRAM.md
Lab Rubrics docs/content/week-01/LAB_RUBRICS.md

You've got this. Your team is lucky to have a lead willing to learn alongside them.